Update app.py

app.py

@@ -3,13 +3,13 @@ import gradio as gr
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
 import spacy
-from spacy.matcher import Matcher 
+
 device='cpu'
 
 processor = AutoProcessor.from_pretrained("microsoft/git-base")
 model = AutoModelForCausalLM.from_pretrained("nkasmanoff/sky-scribe").to(device)
 
-nlp = spacy.load('en_core_web_sm')
+
 
 def predict(image,max_length=50,device='cpu'):
     pixel_values = processor(images=image, return_tensors="pt").to(device).pixel_values
@@ -19,82 +19,6 @@ def predict(image,max_length=50,device='cpu'):
     return generated_caption 
 
 
-def get_entities(sent):
-    ## chunk 1
-    ent1 = ""
-    ent2 = ""
-
-    prv_tok_dep = ""  # dependency tag of previous token in the sentence
-    prv_tok_text = ""  # previous token in the sentence
-
-    prefix = ""
-    modifier = ""
-
-    #############################################################
-
-    for tok in nlp(sent):
-        ## chunk 2
-        # if token is a punctuation mark then move on to the next token
-        if tok.dep_ != "punct":
-            # check: token is a compound word or not
-            if tok.dep_ == "compound":
-                prefix = tok.text
-                # if the previous word was also a 'compound' then add the current word to it
-                if prv_tok_dep == "compound":
-                    prefix = prv_tok_text + " " + tok.text
-
-            # check: token is a modifier or not
-            if tok.dep_.endswith("mod") == True:
-                modifier = tok.text
-                # if the previous word was also a 'compound' then add the current word to it
-                if prv_tok_dep == "compound":
-                    modifier = prv_tok_text + " " + tok.text
-
-            ## chunk 3
-            if tok.dep_.find("subj") == True:
-                ent1 = modifier + " " + prefix + " " + tok.text
-                prefix = ""
-                modifier = ""
-                prv_tok_dep = ""
-                prv_tok_text = ""
-
-                ## chunk 4
-            if tok.dep_.find("obj") == True:
-                ent2 = modifier + " " + prefix + " " + tok.text
-
-            ## chunk 5  
-            # update variables
-            prv_tok_dep = tok.dep_
-            prv_tok_text = tok.text
-    #############################################################
-
-    return [ent1.strip(), ent2.strip()]
-
-
-
-
-def get_relation(sent):
-
-    doc = nlp(sent)
-
-    # Matcher class object 
-    matcher = Matcher(nlp.vocab)
-
-    #define the pattern 
-    pattern = [{'DEP':'ROOT'},
-            {'DEP':'prep','OP':"?"},
-            {'DEP':'agent','OP':"?"},  
-            {'POS':'ADJ','OP':"?"}] 
-
-    matcher.add('matching_pattern', patterns=[pattern])
-    matches = matcher(doc)
-    k = len(matches) - 1
-
-    span = doc[matches[k][1]:matches[k][2]] 
-
-    return(span.text)
-
-
 
 input = gr.inputs.Image(label="Please upload an image", type = 'pil', optional=True)
 output = gr.outputs.Textbox(type="text",label="Captions")